1,721,004 research outputs found
Cellular heterogeneity in DNA alkylation repair increases population genetic plasticity
No full textThe data are described in the article “Cellular heterogeneity in DNA alkylation repair increases population genetic plasticity”. The file names are composed of the strain name (see paper supplementary information) and the drug treatment specific to each experiment. FACS data are in .fcs format and microscopy data are in MATLAB struct array format (.mat) with a separate line for each field of view
Uphoff 2018 PNAS: Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells
No full textThe data are described in the article “Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells”.
The file names are composed of the strain name (see paper supplementary information) and the drug treatment specific to each experiment.
Data are in MATLAB struct array format (.mat) with a separate line for each field of view.
Each line is a struct array containing the following fields for each cell in the field of view:
frames: frame indices
drugTreatment: 1 for frames with drug treatment, 0 otherwise
cellLength: segmented cell length in pixels
cellArea: segmented cell area in pixels
cellDivision: frame indices for cell division events
CFP: average CFP (CFP3A) pixel intensity within segmented cell area (a.u.).
YFP: average YFP (mYPet) pixel intensity within segmented cell area (a.u.)
mCherry: average mCherry (mKate2) pixel intensity within segmented cell area (a.u.)
foci: 1 for frames with a new MutL-mYPet focus, 0 otherwis
A master regulator orchestrates bacterial stress response genes in space and time
No full textThis material relates to the article ‘A master regulator orchestrates bacterial stress response genes in space and time’' by Choudhary et al. The quantitative microscopy data collected from the microfluidics imaging experiments were processed using BACMMAN software (Ollion et al. Nat Protoc. 2019, 3144-3161. doi: 10.1038/s41596-019-0216-9.) and then processed using custom Python code detailed in Choudhary et al. Cell Rep. 2023 (doi: 10.1016/j.celrep.2023.112168) and Choudhary et al. Curr. Bio. 2023 (doi: 10.1016/j.cub.2023.11.002). This folder contains:
(A) Experiment_List: Excel file listing the experiments, including Experiment_ID (enumaerated 1 to 80), Promoter analysed, strain (WT: wild type, or Katg-), H2O2 concentration, Treatment type (step or gradual), treatment time, Start_ROI (Positions greater than this are analysed), and Stop_ROI (Positions smaller than this are analysed).
(B) The output files obtained from BACMMAN for all experiments described in the article by Choudhary et al.
Details of the data collection and analysis procedures can be found in the accompanying article. The folders are named as E1 to E80, corresponding to the experiments listed in Excel file (A) .
Default experiment protocol refers to wild-type E. coli bacteria growing in 1.2 um trenches that were fully loaded and provided with a step treatment of H2O2 and imaged with a time-lag between frames of 3 minutes.
Each of the folders contains subfolders pertaining to different experiments performed in the given conditions. Each subfolder contains BACMMAN output files named as 'SubFolderName'_'0 or 1 or 2 or 3'. Here, 0 relates to measurements of growth channels tracked over time to correct for any drifts while imaging. 1 relates to measurements of the cell mask from the mKate2 cell segmentation marker signal. 2 relates to measurements of CFP or GFP fluorescence inside the segmented cell masks. 3 relates to measurements of YFP fluorescence inside the segmented cell masks (For dual reporter strains). The folder also contains the BACMMAN config file used for each experiment
Lagage et al, 2022: Adaptation delay causes a burst of mutations in bacteria responding to oxidative stress
Full text linkThe data are described in the article “Adaptation delay causes a burst of mutations in bacteria responding to oxidative stress." For microfluidics experiments, datasets are separated in folders, each corresponding to one figure or to different panels of a figure of the article (figure and panel numbers indicated in each folder name). In each folder are the files of the different repeats performed for the indicated figure/panel. The file names are composed of the name of the strain, and of the frame number corresponding to the start of hydrogen peroxide treatment specific to each experiment. Data are in MATLAB struct array format (.mat) with a separate line for each field of view. Each line is a struct array containing the following fields for each cell in the field of view: cellNo (number of the cell), area (segmented cell area in pixels), Frame No (number of the frame, frame 0 is the first frame of the experiment), MajorAxislength (in pixels), MinorAxislength (in pixels), cenX (centroid X), CenY (centroid Y), mCherryAvg (average mCherry (mKate2) pixel intensity within segmented cell area (a.u.)), mCherrytot (total mCherry (mKate2) pixel intensity, within segmented cell area (a.u.)). YFPAvg (average YFP pixel intensity within segmented cell area (a.u.)), YFPTot (total YFP pixel intensity, within segmented cell area (a.u.)), CFPAvg (average CFP pixel intensity within segmented cell area (a.u.)), CFPTot (total CFP pixel intensity, within segmented cell area (a.u.)), fociNo (number of foci for each frame (1 for frames with a new MutL-mYPet focus, 0 otherwise))), fociIntensity (pixel intensity within foci area (a.u.)), divTimes (frame at which cell division occurs), divDurations (number of frames in between two divisions), CellPhase (fraction of cell cycle at each frame (starting from 0 after division and until 1 just before the next division)), divLeftOver (number of frames left after the last cell division at the end of the experiment). For single-molecule experiments, the folders contain .tracks files labelled with the name of the strain and the time of hydrogen peroxide treatment when needed. We have also included an Excel file named “fractionofboundmoleculesoxyrhalo-pol1halo-dfurpol1halo”, containing the value of the fraction of bound molecules for the relevant strains in untreated and hydrogen peroxide-treated conditions. A MATLAB file named “Matlab figure plotting scripts“ contains the scripts to plot the microfluidics data (generation time, mismatch rate plot, CFP average response curves, survival time distribution, elongation rate, CFP expression rate plots, cell length) and a script to plot diffusion coefficient histograms from the tracks files. For rifampicin assays, the number of colonies counted on LB and LB + rifampicin plates for each repeat are given in an excel file named “rifassaydata-figEV3”
Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions
Full text linkThis material relates to the article 'Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions' by Choudhary et al. The quantitative microscopy data collected from the microfluidics imaging experiments were processed using BACMMAN software (Ollion et al. Nat Protoc. 2019, 3144-3161. doi: 10.1038/s41596-019-0216-9.) and then processed using custom Python code. This folder contains:
(A) The output files obtained from BACMMAN for all experiments described in the article by Choudhary et al.
(B) Python codes that were used to generate the data plots in the article by Choudhary et al .
Details of the data collection and analysis procedures can be found in the accompanying article.The folders are named as 'Concentration of H2O2 used'_'Promoter'_'Any changes to default experiment protocol'.
Default experiment protocol refers to wild-type E. coli bacteria growing in 1.2 um trenches that were fully loaded and provided with a step treatment of H2O2 and imaged with a time-lag between frames of 3 minutes.
These folders are :
(1)100uM_Pahpc_default
(2)100uM_PgrxA_1p4
(3)100uM_PgrxA_1p4,lowload
(4)100uM_PgrxA_45secResolution
(5)100uM_PgrxA_default
(6)100uM_PgrxA_DoxyRandWTmix
(7)100uM_PgrxA_InactiveWTmix
(8)100uM_PgrxA_Lowloading
(9)100um_PgrxA_oxyRMutant
(10)100uM_PgrxA_Pulses
(11)100uM_Pkatg_default
(12)500uM_PgrxA_grad
(13)500uM_PgrxA_PIStain
(14)500uM_PgrxA_step
Each of the folders contains subfolders pertaining to different experiments performed in the given conditions. Each subfolder contains BACMMAN output files named as 'SubFolderName'_'0 or 1 or 2 or 3'. Here, 0 relates to measurements of growth channels tracked over time to correct for any drifts while imaging. 1 relates to measurements of the cell mask from the mKate2 cell segmentation marker signal. 2 relates to measurements of CFP fluorescence inside the segmented cell masks. 3 relates to MutL-mYPet foci detection in the segmented cell masks. The folder also contains the BACMMAN config file used for each experiment.
The other folders:
(15) ExperimentsForCalibration: contains the BACMMAN output files of the experiments that were used for calibration experiments performed at different H2O2 concentrations.
(16) ExperimentsForMachineLearningAnalysis: contains BACMMAN output files of experiments for Machine Learning Analysis.
(17) Experiment_details.CSV contains details of each experiment, such as the time of H2O2 treatment and the ROIs used in analysis (annotated as ‘Position’ in the data files).
(18) Figures_codes : contains Python codes for generating the figures in accompanying article.
The subheading in each code file correspond to different panels within the same figures
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Investigating the communication of mismatch recognition and strand incision in the DNA mismatch repair pathway
No full textDNA Mismatch Repair (MMR) is crucial for preventing DNA replication errors that escape the proofreading mechanisms of DNA polymerases from becoming permanent mutations. MMR is initiated when MutS recognises a mismatch and recruits MutL to signal the degradation and resynthesis of the nascent DNA. The distinction between the nascent and template DNA strands is a critical aspect of Mismatch Repair in all organisms. In Escherichia coli, this problem is solved by MutH endonuclease, which incises the nascent strand specifically at hemimethylated GATC sequences. However, it remains unclear how the MutS-MutL complex communicates with MutH to activate strand incision, given that GATC sites can be located hundreds of bases away from a mismatch. To tackle this long standing question, we used single-molecule tracking of fluorescently-labeledMutS,MutL, andMutH proteins in live E. coli cells. Our quantitative measurements address how MutH is recruited to hemimethylated GATC sites. We find that it is neither dependent on MutS/L nor on the presence of DNA mismatches suggesting that MutH binds to hemimethylated GATC sites independently and becomes activated by interaction withMutL. Furthermore, we show that MutH competes with SeqA and Dam proteins when binding to GATC sites. Hence, there is a brief time window during which MutH has to be activated before the strand discrimination signal is lost. We used Fluorescence Recovery after Photobleaching (FRAP) to measure the stoichiometry and turnover of proteins during Mismatch Repair. Our results complement previous studies, leading to a refined model for the coordination of the E. coli Mismatch Repair pathway in vivo
Individual and collective behaviour of bacteria under oxidative stress
Full text linkBacteria, the most abundant organisms supporting life on Earth, inhabit various niches, from soil and aquatic life, to residing in hosts. Bacteria frequently experience oxidative stress during aerobic growth, host immune attack, microbiome interactions, and under antibiotic treatment. Here, we study bacteria under hydrogen peroxide (H2O2) and observe that individual cells exhibit variability in their oxidative stress response. Such diversification of cellular phenotype is often observed in response to changing environments, but the underlying causes of cell-cell variability and its implications for cellular function in the context of stress adaptation remain unclear. Cellular response variability is typically attributed to stochasticity in biochemical reactions inside a cell. Our machine learning algorithm reveals that the variability in cellular response is not stochastic but determined by the precise response of cells to dynamic levels of H2O2, created by short-ranged cell-cell interactions. We complement single-cell time-lapse imaging with mathematical modeling to probe the consequences of single-cell variability on population adaptation. We find that the H2O2 scavenging activity of cells creates strong H2O2 gradients, thereby protecting a large proportion of the population. We show that the gene expression fluctuations of individual cells during constant H2O2 treatment are in fact driven by chaos. Although it has been suggested that chaos may play a role in many biological phenomena, it has remained challenging to disentangle chaos from noise as a source of variability in biological data. The close correspondence between our experiments and model allowed us to show that chaos emerges from deterministic feedbacks between cells and their environment. These feedbacks amplify small differences in initial conditions, resulting in diverging stress response dynamics that lead to seemingly random phenotypic variability. Next, we investigate how individual cells manage to regulate the levels of many diverse genes that encode oxidative stress tolerance factors. We find that the dynamics of over two dozen genes create a diversity of spatiotemporal expression patterns that benefit the stress adaptation of a cell population. Overall, we show that bacterial stress responses can generate variability under stress via deterministic factors without noise. Our work provides a general approach for uncovering the hidden variables that drive variability in cellular responses to environmental changes and for probing the regulatory mechanisms from molecular, single-cell, and population perspectives
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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